{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier with Grid Search\n",
    "Decision tree is trained on feature set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 16) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 16) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_train_std.shape, \"and labels: \", y_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_test_std), X_test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 731.00 minutes \n",
      "   \n",
      "Result of grid search:\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=12,\n",
      "            max_features=None, max_leaf_nodes=149,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=42, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "params = {'max_depth': list(range(9,18)),'max_leaf_nodes': list(range(50, 150)), 'min_samples_split': [2,3,4],\n",
    "          'max_features': [None,'auto','sqrt','log2'], 'criterion':['gini', 'entropy']}\n",
    "\n",
    "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, verbose=0)\n",
    "\n",
    "start = time()\n",
    "grid_search_cv.fit(X_train_std, y_train)\n",
    "print(\"Grid search took %.2f minutes \"%((time() - start)//60))\n",
    "\n",
    "print(\"   \")\n",
    "\n",
    "print(\"Result of grid search:\")\n",
    "print(grid_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "Decision Tree's accuracy on -20 dB SNR samples =  0.1245\n",
      "Decision Tree's accuracy on -18 dB SNR samples =  0.126\n",
      "Decision Tree's accuracy on -16 dB SNR samples =  0.12925\n",
      "Decision Tree's accuracy on -14 dB SNR samples =  0.12775\n",
      "Decision Tree's accuracy on -12 dB SNR samples =  0.1415\n",
      "Decision Tree's accuracy on -10 dB SNR samples =  0.16025\n",
      "Decision Tree's accuracy on -8 dB SNR samples =  0.24275\n",
      "Decision Tree's accuracy on -6 dB SNR samples =  0.3325\n",
      "Decision Tree's accuracy on -4 dB SNR samples =  0.41025\n",
      "Decision Tree's accuracy on -2 dB SNR samples =  0.43325\n",
      "Decision Tree's accuracy on 0 dB SNR samples =  0.50475\n",
      "Decision Tree's accuracy on 2 dB SNR samples =  0.62075\n",
      "Decision Tree's accuracy on 4 dB SNR samples =  0.75075\n",
      "Decision Tree's accuracy on 6 dB SNR samples =  0.793\n",
      "Decision Tree's accuracy on 8 dB SNR samples =  0.80975\n",
      "Decision Tree's accuracy on 10 dB SNR samples =  0.81825\n",
      "Decision Tree's accuracy on 12 dB SNR samples =  0.827\n",
      "Decision Tree's accuracy on 14 dB SNR samples =  0.825\n",
      "Decision Tree's accuracy on 16 dB SNR samples =  0.8115\n",
      "Decision Tree's accuracy on 18 dB SNR samples =  0.81775\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = grid_search_cv.predict(X_test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_test[snr], y_pred[snr])\n",
    "    print(\"Decision Tree's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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41AoJ+fn5uHnzJk6fPo3ffvtNrcC1tLSs9a5kqqqO4vr7+9d4QVPHjh3Rpk0b5WPVubx2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d200d69320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    439     0      0     0     0     42     39     3\n",
      "BPSK      1   483      0     0    12      4      7     2\n",
      "CPFSK    12     0    461    15     0      5      8     0\n",
      "GFSK      9     1     39   485     1     10     11     1\n",
      "PAM4      1    12      0     0   484      4      7     2\n",
      "QAM16     6     0      0     0     0    219    200     3\n",
      "QAM64     5     0      0     0     0    189    215     4\n",
      "QPSK     27     4      0     0     3     27     13   485\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = grid_search_cv.predict(X_test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.84  0.00   0.00  0.00  0.00   0.08   0.07  0.01\n",
      "BPSK   0.00  0.95   0.00  0.00  0.02   0.01   0.01  0.00\n",
      "CPFSK  0.02  0.00   0.92  0.03  0.00   0.01   0.02  0.00\n",
      "GFSK   0.02  0.00   0.07  0.87  0.00   0.02   0.02  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.95   0.01   0.01  0.00\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.51   0.47  0.01\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.46   0.52  0.01\n",
      "QPSK   0.05  0.01   0.00  0.00  0.01   0.05   0.02  0.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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IkjYADgRmR8Q/G1w/MzPrBUSV7xRbtP+0loE2V2THWZLWIa1IPhB4R7a23HmN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\n+Pb1VHWNSR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89847e0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_tree2.pkl']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(grid_search_cv, \"decision_tree2.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "grid_search_cv = joblib.load(\"decision_tree2.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
